Apache Spark

Course Overview

Apache Spark is a high‑performance, distributed computing course designed to teach learners how to process large‑scale datasets efficiently. The course covers Spark’s core architecture, RDDs, DataFrames, Spark SQL, structured streaming, and machine‑learning pipelines. Learners gain hands‑on experience building scalable data‑processing workflows using PySpark and working with Spark on local, cluster, and cloud environments.

Target Audience

This course is ideal for:

• Aspiring data engineers and big‑data developers

• Data analysts and data scientists working with large datasets

• Software engineers expanding into distributed systems

• Students or career switchers entering big‑data engineering

• Anyone who wants to master Spark for ETL, analytics, or machine learning at scale

Course Outcomes

By the end of this course, learners will be able to:

• Understand Spark architecture: drivers, executors, clusters, DAGs, and lazy evaluation

• Work with RDDs, DataFrames, and Spark SQL for large‑scale data processing

• Build ETL pipelines using PySpark

• Optimize Spark jobs using partitioning, caching, and performance tuning

• Process streaming data using Spark Structured Streaming

• Use Spark MLlib to build scalable machine‑learning workflows

• Deploy Spark applications on clusters (YARN, Kubernetes, Databricks, EMR)

• Apply Spark to real‑world data‑engineering and analytics scenarios